'''simple docstring'''





import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class 			__A (					tf.keras.layers.Layer      ):



    def __init__(self       :       Any  ,     __a       :       Dict[str, int]  ,     __a       :       List[str]  ,     __a       :       int = None  ,     __a       :       int = None					):
         super().__init__()
         UpperCAmelCase_			       =  pad_token_id
         UpperCAmelCase_			       =  max_length
         UpperCAmelCase_			       =  vocab
         UpperCAmelCase_			       =  merges
         UpperCAmelCase_			       =  BytePairTokenizer(__a  ,     __a  ,     sequence_length=__a					)



    @classmethod
    def    _lowercase		(cls       :       str  ,     __a       :       GPTaTokenizer  ,     *__a       :       Tuple  ,     **__a       :       Tuple					):
         UpperCAmelCase_			       =  [" ".join(__a					) for m in tokenizer.bpe_ranks.keys()]
         UpperCAmelCase_			       =  tokenizer.get_vocab()
         return cls(__a  ,     __a  ,     *__a  ,     **__a					)



    @classmethod
    def    _lowercase		(cls       :       Union[str, Any]  ,     __a       :       Union[str, os.PathLike]  ,     *__a       :       str  ,     **__a       :       Union[str, Any]					):
         UpperCAmelCase_			       =  GPTaTokenizer.from_pretrained(__a  ,     *__a  ,     **__a					)
         return cls.from_tokenizer(__a  ,     *__a  ,     **__a					)



    @classmethod
    def    _lowercase		(cls       :       Optional[Any]  ,     __a       :       Any					):
         return cls(**__a					)



    def    _lowercase		(self       :       str					):
         return {
             "vocab": self.vocab,
             "merges": self.merges,
             "max_length": self.max_length,
             "pad_token_id": self.pad_token_id,
         }






    def    _lowercase		(self       :       str  ,     __a       :       Any  ,     __a       :       int = None					):
         UpperCAmelCase_			       =  self.tf_tokenizer(__a					)
         UpperCAmelCase_			       =  tf.ones_like(__a					)

         if self.pad_token_id is not None:
              # pad the tokens up to max length
              UpperCAmelCase_			       =  max_length if max_length is not None else self.max_length

              if max_length is not None:
                   UpperCAmelCase_       ,					UpperCAmelCase_			       =  pad_model_inputs(
                       __a  ,     max_seq_length=__a  ,     pad_value=self.pad_token_id					)

         return {"attention_mask": attention_mask, "input_ids": input_ids}







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


